Mercurial > ift6266
annotate writeup/nips2010_submission.tex @ 587:b1be957dd1be
Added mlj_submission to group every file needed for that.
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date | Thu, 30 Sep 2010 17:51:02 -0400 |
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1 \documentclass{article} % For LaTeX2e |
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2 \usepackage{nips10submit_e,times} |
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3 \usepackage{wrapfig} |
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4 \usepackage{amsthm,amsmath,bbm} |
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5 \usepackage[psamsfonts]{amssymb} |
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6 \usepackage{algorithm,algorithmic} |
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7 \usepackage[utf8]{inputenc} |
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8 \usepackage{graphicx,subfigure} |
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523 | 11 %\setlength\parindent{0mm} |
12 | |
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13 \title{Deep Self-Taught Learning for Handwritten Character Recognition} |
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14 \author{ |
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15 Frédéric Bastien , |
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16 Yoshua Bengio , |
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17 Arnaud Bergeron , |
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18 Nicolas Boulanger-Lewandowski , |
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19 Thomas Breuel , |
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20 Youssouf Chherawala , |
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21 Moustapha Cisse , |
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22 Myriam Côté , |
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23 Dumitru Erhan , |
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24 Jeremy Eustache , |
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25 Xavier Glorot , |
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26 Xavier Muller , |
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27 Sylvain Pannetier Lebeuf , |
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28 Razvan Pascanu , |
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29 Salah Rifai , |
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30 Francois Savard , |
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31 Guillaume Sicard |
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32 } |
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33 |
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34 \begin{document} |
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35 |
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36 %\makeanontitle |
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37 \maketitle |
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38 |
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39 \vspace*{-2mm} |
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40 \begin{abstract} |
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41 Recent theoretical and empirical work in statistical machine learning has |
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42 demonstrated the importance of learning algorithms for deep |
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43 architectures, i.e., function classes obtained by composing multiple |
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44 non-linear transformations. Self-taught learning (exploiting unlabeled |
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45 examples or examples from other distributions) has already been applied |
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46 to deep learners, but mostly to show the advantage of unlabeled |
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47 examples. Here we explore the advantage brought by {\em out-of-distribution examples}. |
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48 For this purpose we |
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49 developed a powerful generator of stochastic variations and noise |
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50 processes for character images, including not only affine transformations |
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51 but also slant, local elastic deformations, changes in thickness, |
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52 background images, grey level changes, contrast, occlusion, and various |
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53 types of noise. The out-of-distribution examples are obtained from these |
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54 highly distorted images or by including examples of object classes |
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55 different from those in the target test set. |
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56 We show that {\em deep learners benefit |
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57 more from them than a corresponding shallow learner}, at least in the area of |
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58 handwritten character recognition. In fact, we show that they reach |
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59 human-level performance on both handwritten digit classification and |
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60 62-class handwritten character recognition. |
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61 \end{abstract} |
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62 \vspace*{-3mm} |
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63 |
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64 \section{Introduction} |
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65 \vspace*{-1mm} |
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66 |
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67 {\bf Deep Learning} has emerged as a promising new area of research in |
469 | 68 statistical machine learning (see~\citet{Bengio-2009} for a review). |
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69 Learning algorithms for deep architectures are centered on the learning |
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70 of useful representations of data, which are better suited to the task at hand. |
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71 This is in part inspired by observations of the mammalian visual cortex, |
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72 which consists of a chain of processing elements, each of which is associated with a |
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73 different representation of the raw visual input. In fact, |
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74 it was found recently that the features learnt in deep architectures resemble |
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75 those observed in the first two of these stages (in areas V1 and V2 |
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76 of visual cortex)~\citep{HonglakL2008}, and that they become more and |
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77 more invariant to factors of variation (such as camera movement) in |
501 | 78 higher layers~\citep{Goodfellow2009}. |
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79 Learning a hierarchy of features increases the |
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80 ease and practicality of developing representations that are at once |
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81 tailored to specific tasks, yet are able to borrow statistical strength |
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82 from other related tasks (e.g., modeling different kinds of objects). Finally, learning the |
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83 feature representation can lead to higher-level (more abstract, more |
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84 general) features that are more robust to unanticipated sources of |
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85 variance extant in real data. |
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86 |
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87 {\bf Self-taught learning}~\citep{RainaR2007} is a paradigm that combines principles |
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88 of semi-supervised and multi-task learning: the learner can exploit examples |
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89 that are unlabeled and possibly come from a distribution different from the target |
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90 distribution, e.g., from other classes than those of interest. |
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91 It has already been shown that deep learners can clearly take advantage of |
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92 unsupervised learning and unlabeled examples~\citep{Bengio-2009,WestonJ2008-small}, |
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93 but more needs to be done to explore the impact |
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94 of {\em out-of-distribution} examples and of the multi-task setting |
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95 (one exception is~\citep{CollobertR2008}, which uses a different kind |
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96 of learning algorithm). In particular the {\em relative |
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97 advantage} of deep learning for these settings has not been evaluated. |
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98 The hypothesis discussed in the conclusion is that a deep hierarchy of features |
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99 may be better able to provide sharing of statistical strength |
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100 between different regions in input space or different tasks. |
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101 |
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102 \iffalse |
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103 Whereas a deep architecture can in principle be more powerful than a |
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104 shallow one in terms of representation, depth appears to render the |
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105 training problem more difficult in terms of optimization and local minima. |
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106 It is also only recently that successful algorithms were proposed to |
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107 overcome some of these difficulties. All are based on unsupervised |
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108 learning, often in an greedy layer-wise ``unsupervised pre-training'' |
469 | 109 stage~\citep{Bengio-2009}. One of these layer initialization techniques, |
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110 applied here, is the Denoising |
547 | 111 Auto-encoder~(DA)~\citep{VincentPLarochelleH2008-very-small} (see Figure~\ref{fig:da}), |
112 which | |
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113 performed similarly or better than previously proposed Restricted Boltzmann |
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114 Machines in terms of unsupervised extraction of a hierarchy of features |
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115 useful for classification. Each layer is trained to denoise its |
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116 input, creating a layer of features that can be used as input for the next layer. |
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117 \fi |
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118 %The principle is that each layer starting from |
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119 %the bottom is trained to encode its input (the output of the previous |
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120 %layer) and to reconstruct it from a corrupted version. After this |
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121 %unsupervised initialization, the stack of DAs can be |
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122 %converted into a deep supervised feedforward neural network and fine-tuned by |
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123 %stochastic gradient descent. |
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124 |
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125 % |
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126 In this paper we ask the following questions: |
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127 |
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128 %\begin{enumerate} |
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129 $\bullet$ %\item |
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130 Do the good results previously obtained with deep architectures on the |
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131 MNIST digit images generalize to the setting of a much larger and richer (but similar) |
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132 dataset, the NIST special database 19, with 62 classes and around 800k examples? |
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133 |
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134 $\bullet$ %\item |
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135 To what extent does the perturbation of input images (e.g. adding |
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136 noise, affine transformations, background images) make the resulting |
484
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137 classifiers better not only on similarly perturbed images but also on |
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138 the {\em original clean examples}? We study this question in the |
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139 context of the 62-class and 10-class tasks of the NIST special database 19. |
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140 |
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141 $\bullet$ %\item |
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142 Do deep architectures {\em benefit more from such out-of-distribution} |
469 | 143 examples, i.e. do they benefit more from the self-taught learning~\citep{RainaR2007} framework? |
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144 We use highly perturbed examples to generate out-of-distribution examples. |
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145 |
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146 $\bullet$ %\item |
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147 Similarly, does the feature learning step in deep learning algorithms benefit more |
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148 from training with moderately different classes (i.e. a multi-task learning scenario) than |
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149 a corresponding shallow and purely supervised architecture? |
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150 We train on 62 classes and test on 10 (digits) or 26 (upper case or lower case) |
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151 to answer this question. |
484
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152 %\end{enumerate} |
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153 |
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154 Our experimental results provide positive evidence towards all of these questions. |
547 | 155 To achieve these results, we introduce in the next section a sophisticated system |
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156 for stochastically transforming character images and then explain the methodology, |
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157 which is based on training with or without these transformed images and testing on |
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158 clean ones. We measure the relative advantage of out-of-distribution examples |
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159 for a deep learner vs a supervised shallow one. |
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160 Code for generating these transformations as well as for the deep learning |
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161 algorithms are made available. |
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162 We also estimate the relative advantage for deep learners of training with |
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163 other classes than those of interest, by comparing learners trained with |
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164 62 classes with learners trained with only a subset (on which they |
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165 are then tested). |
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166 The conclusion discusses |
547 | 167 the more general question of why deep learners may benefit so much from |
168 the self-taught learning framework. | |
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169 |
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170 \vspace*{-3mm} |
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171 \section{Perturbation and Transformation of Character Images} |
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172 \label{s:perturbations} |
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173 \vspace*{-2mm} |
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174 |
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175 \begin{wrapfigure}[8]{l}{0.15\textwidth} |
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176 %\begin{minipage}[b]{0.14\linewidth} |
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177 \vspace*{-5mm} |
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178 \begin{center} |
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179 \includegraphics[scale=.4]{images/Original.png}\\ |
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180 {\bf Original} |
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181 \end{center} |
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182 \end{wrapfigure} |
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183 %\vspace{0.7cm} |
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184 %\end{minipage}% |
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185 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
467 | 186 This section describes the different transformations we used to stochastically |
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187 transform $32 \times 32$ source images (such as the one on the left) |
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188 in order to obtain data from a larger distribution which |
547 | 189 covers a domain substantially larger than the clean characters distribution from |
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190 which we start. |
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191 Although character transformations have been used before to |
547 | 192 improve character recognizers, this effort is on a large scale both |
193 in number of classes and in the complexity of the transformations, hence | |
194 in the complexity of the learning task. | |
195 More details can | |
469 | 196 be found in this technical report~\citep{ift6266-tr-anonymous}. |
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197 The code for these transformations (mostly python) is available at |
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198 {\tt http://anonymous.url.net}. All the modules in the pipeline share |
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199 a global control parameter ($0 \le complexity \le 1$) that allows one to modulate the |
467 | 200 amount of deformation or noise introduced. |
201 There are two main parts in the pipeline. The first one, | |
202 from slant to pinch below, performs transformations. The second | |
203 part, from blur to contrast, adds different kinds of noise. | |
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204 %\end{minipage} |
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205 |
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206 \vspace*{1mm} |
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207 %\subsection{Transformations} |
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208 {\large\bf 2.1 Transformations} |
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209 \vspace*{1mm} |
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210 |
501 | 211 |
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212 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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213 %\begin{minipage}[b]{0.14\linewidth} |
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214 %\centering |
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215 \begin{center} |
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216 \vspace*{-5mm} |
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217 \includegraphics[scale=.4]{images/Thick_only.png}\\ |
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218 {\bf Thickness} |
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219 \end{center} |
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220 %\vspace{.6cm} |
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221 %\end{minipage}% |
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222 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
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223 \end{wrapfigure} |
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224 To change character {\bf thickness}, morphological operators of dilation and erosion~\citep{Haralick87,Serra82} |
467 | 225 are applied. The neighborhood of each pixel is multiplied |
226 element-wise with a {\em structuring element} matrix. | |
227 The pixel value is replaced by the maximum or the minimum of the resulting | |
228 matrix, respectively for dilation or erosion. Ten different structural elements with | |
229 increasing dimensions (largest is $5\times5$) were used. For each image, | |
230 randomly sample the operator type (dilation or erosion) with equal probability and one structural | |
541 | 231 element from a subset of the $n=round(m \times complexity)$ smallest structuring elements |
232 where $m=10$ for dilation and $m=6$ for erosion (to avoid completely erasing thin characters). | |
233 A neutral element (no transformation) | |
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234 is always present in the set. |
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235 %\vspace{.4cm} |
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236 %\end{minipage} |
582 | 237 \vspace*{3mm} |
523 | 238 |
551
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239 \begin{minipage}[b]{0.14\linewidth} |
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240 \centering |
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241 \includegraphics[scale=.4]{images/Slant_only.png}\\ |
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242 {\bf Slant} |
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243 \end{minipage}% |
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244 \hspace{0.3cm} |
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245 \begin{minipage}[b]{0.83\linewidth} |
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246 %\centering |
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247 To produce {\bf slant}, each row of the image is shifted |
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248 proportionally to its height: $shift = round(slant \times height)$. |
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249 $slant \sim U[-complexity,complexity]$. |
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250 The shift is randomly chosen to be either to the left or to the right. |
582 | 251 \vspace{8mm} |
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252 \end{minipage} |
582 | 253 \vspace*{3mm} |
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254 |
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255 %\begin{minipage}[b]{0.14\linewidth} |
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256 %\centering |
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257 \begin{wrapfigure}[8]{l}{0.15\textwidth} |
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258 \vspace*{-6mm} |
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259 \begin{center} |
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260 \includegraphics[scale=.4]{images/Affine_only.png}\\ |
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261 {\small {\bf Affine \mbox{Transformation}}} |
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262 \end{center} |
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263 \end{wrapfigure} |
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264 %\end{minipage}% |
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265 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
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266 A $2 \times 3$ {\bf affine transform} matrix (with |
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267 parameters $(a,b,c,d,e,f)$) is sampled according to the $complexity$. |
541 | 268 Output pixel $(x,y)$ takes the value of input pixel |
269 nearest to $(ax+by+c,dx+ey+f)$, | |
270 producing scaling, translation, rotation and shearing. | |
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271 Marginal distributions of $(a,b,c,d,e,f)$ have been tuned to |
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272 forbid large rotations (to avoid confusing classes) but to give good |
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273 variability of the transformation: $a$ and $d$ $\sim U[1-3 |
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274 complexity,1+3\,complexity]$, $b$ and $e$ $\sim U[-3 \,complexity,3\, |
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275 complexity]$, and $c$ and $f \sim U[-4 \,complexity, 4 \, |
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276 complexity]$.\\ |
582 | 277 \end{minipage} |
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278 |
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279 \vspace*{-4.5mm} |
523 | 280 |
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281 \begin{minipage}[t]{\linewidth} |
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282 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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283 %\hspace*{-8mm}\begin{minipage}[b]{0.25\linewidth} |
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284 %\centering |
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285 \begin{center} |
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286 \vspace*{-4mm} |
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287 \includegraphics[scale=.4]{images/Localelasticdistorsions_only.png}\\ |
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288 {\bf Local Elastic Deformation} |
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289 \end{center} |
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290 \end{wrapfigure} |
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291 %\end{minipage}% |
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292 %\hspace{-3mm}\begin{minipage}[b]{0.85\linewidth} |
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293 %\vspace*{-20mm} |
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294 The {\bf local elastic deformation} |
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295 module induces a ``wiggly'' effect in the image, following~\citet{SimardSP03-short}, |
467 | 296 which provides more details. |
541 | 297 The intensity of the displacement fields is given by |
298 $\alpha = \sqrt[3]{complexity} \times 10.0$, which are | |
299 convolved with a Gaussian 2D kernel (resulting in a blur) of | |
300 standard deviation $\sigma = 10 - 7 \times\sqrt[3]{complexity}$. | |
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301 %\vspace{.9cm} |
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302 \end{minipage} |
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303 |
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304 \vspace*{7mm} |
523 | 305 |
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306 %\begin{minipage}[b]{0.14\linewidth} |
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307 %\centering |
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308 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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309 \vspace*{-5mm} |
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310 \begin{center} |
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311 \includegraphics[scale=.4]{images/Pinch_only.png}\\ |
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312 {\bf Pinch} |
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313 \end{center} |
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314 \end{wrapfigure} |
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315 %\vspace{.6cm} |
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316 %\end{minipage}% |
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317 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
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318 The {\bf pinch} module applies the ``Whirl and pinch'' GIMP filter with whirl set to 0. |
541 | 319 A pinch is ``similar to projecting the image onto an elastic |
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320 surface and pressing or pulling on the center of the surface'' (GIMP documentation manual). |
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321 For a square input image, draw a radius-$r$ disk |
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322 around its center $C$. Any pixel $P$ belonging to |
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323 that disk has its value replaced by |
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324 the value of a ``source'' pixel in the original image, |
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325 on the line that goes through $C$ and $P$, but |
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326 at some other distance $d_2$. Define $d_1=distance(P,C)$ |
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327 and $d_2 = sin(\frac{\pi{}d_1}{2r})^{-pinch} \times |
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328 d_1$, where $pinch$ is a parameter of the filter. |
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329 The actual value is given by bilinear interpolation considering the pixels |
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330 around the (non-integer) source position thus found. |
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331 Here $pinch \sim U[-complexity, 0.7 \times complexity]$. |
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332 %\vspace{1.5cm} |
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333 %\end{minipage} |
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334 |
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335 \vspace{1mm} |
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336 |
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337 {\large\bf 2.2 Injecting Noise} |
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338 %\subsection{Injecting Noise} |
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339 \vspace{2mm} |
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340 |
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341 %\vspace*{-.2cm} |
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342 \begin{minipage}[t]{0.14\linewidth} |
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343 \centering |
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344 \vspace*{-2mm} |
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345 \includegraphics[scale=.4]{images/Motionblur_only.png}\\ |
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346 {\bf Motion Blur} |
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347 \end{minipage}% |
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348 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
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349 %\vspace*{.5mm} |
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350 The {\bf motion blur} module is GIMP's ``linear motion blur'', which |
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351 has parameters $length$ and $angle$. The value of |
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352 a pixel in the final image is approximately the mean of the first $length$ pixels |
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353 found by moving in the $angle$ direction, |
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354 $angle \sim U[0,360]$ degrees, and $length \sim {\rm Normal}(0,(3 \times complexity)^2)$. |
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355 \vspace{5mm} |
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356 \end{minipage} |
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357 |
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358 \vspace*{1mm} |
523 | 359 |
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360 \begin{minipage}[t]{0.14\linewidth} |
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361 \centering |
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362 \includegraphics[scale=.4]{images/occlusion_only.png}\\ |
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363 {\bf Occlusion} |
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364 %\vspace{.5cm} |
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365 \end{minipage}% |
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366 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
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367 \vspace*{-18mm} |
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368 The {\bf occlusion} module selects a random rectangle from an {\em occluder} character |
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369 image and places it over the original {\em occluded} |
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370 image. Pixels are combined by taking the max(occluder, occluded), |
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|
371 i.e. keeping the lighter ones. |
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Olivier Delalleau <delallea@iro>
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372 The rectangle corners |
467 | 373 are sampled so that larger complexity gives larger rectangles. |
374 The destination position in the occluded image are also sampled | |
544
1cdfc17e890f
ca fitte maintenant
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parents:
541
diff
changeset
|
375 according to a normal distribution (more details in~\citet{ift6266-tr-anonymous}). |
557
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parents:
555
diff
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|
376 This module is skipped with probability 60\%. |
555
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parents:
554
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|
377 %\vspace{7mm} |
551
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parents:
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378 \end{minipage} |
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Frederic Bastien <nouiz@nouiz.org>
parents:
550
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|
379 |
555
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parents:
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diff
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380 \vspace*{1mm} |
523 | 381 |
555
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parents:
554
diff
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|
382 \begin{wrapfigure}[8]{l}{0.15\textwidth} |
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parents:
554
diff
changeset
|
383 \vspace*{-6mm} |
b6dfba0a110c
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parents:
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diff
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|
384 \begin{center} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
385 %\begin{minipage}[t]{0.14\linewidth} |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
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|
386 %\centering |
558
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Olivier Delalleau <delallea@iro>
parents:
555
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387 \includegraphics[scale=.4]{images/Bruitgauss_only.png}\\ |
555
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
388 {\bf Gaussian Smoothing} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
389 \end{center} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
390 \end{wrapfigure} |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
391 %\vspace{.5cm} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
392 %\end{minipage}% |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
393 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
394 With the {\bf Gaussian smoothing} module, |
560
dc5c3f538a05
Small fixes (typos / precisions)
Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
395 different regions of the image are spatially smoothed. |
dc5c3f538a05
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Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
396 This is achieved by first convolving |
dc5c3f538a05
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Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
397 the image with an isotropic Gaussian kernel of |
495 | 398 size and variance chosen uniformly in the ranges $[12,12 + 20 \times |
560
dc5c3f538a05
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Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
399 complexity]$ and $[2,2 + 6 \times complexity]$. This filtered image is normalized |
dc5c3f538a05
Small fixes (typos / precisions)
Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
400 between $0$ and $1$. We also create an isotropic weighted averaging window, of the |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
401 kernel size, with maximum value at the center. For each image we sample |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
402 uniformly from $3$ to $3 + 10 \times complexity$ pixels that will be |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
403 averaging centers between the original image and the filtered one. We |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
404 initialize to zero a mask matrix of the image size. For each selected pixel |
560
dc5c3f538a05
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Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
405 we add to the mask the averaging window centered on it. The final image is |
dc5c3f538a05
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Olivier Delalleau <delallea@iro>
parents:
559
diff
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|
406 computed from the following element-wise operation: $\frac{image + filtered\_image |
dc5c3f538a05
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Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
407 \times mask}{mask+1}$. |
557
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encore du visuel de Myriam
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parents:
555
diff
changeset
|
408 This module is skipped with probability 75\%. |
555
b6dfba0a110c
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parents:
554
diff
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|
409 %\end{minipage} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
410 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
411 \newpage |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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diff
changeset
|
412 |
b6dfba0a110c
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parents:
554
diff
changeset
|
413 \vspace*{-9mm} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
414 |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
415 %\hspace*{-3mm}\begin{minipage}[t]{0.18\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
416 %\centering |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
417 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
418 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
419 \vspace*{-5mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
420 \begin{center} |
558
143a1467f157
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Olivier Delalleau <delallea@iro>
parents:
555
diff
changeset
|
421 \includegraphics[scale=.4]{images/Permutpixel_only.png}\\ |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
422 {\small\bf Permute Pixels} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
423 \end{center} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
424 \end{wrapfigure} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
425 %\end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
426 %\hspace{-0cm}\begin{minipage}[t]{0.86\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
427 %\vspace*{-20mm} |
560
dc5c3f538a05
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Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
428 This module {\bf permutes neighbouring pixels}. It first selects a |
dc5c3f538a05
Small fixes (typos / precisions)
Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
429 fraction $\frac{complexity}{3}$ of pixels randomly in the image. Each |
dc5c3f538a05
Small fixes (typos / precisions)
Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
430 of these pixels is then sequentially exchanged with a random pixel |
dc5c3f538a05
Small fixes (typos / precisions)
Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
431 among its four nearest neighbors (on its left, right, top or bottom). |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
432 This module is skipped with probability 80\%.\\ |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
433 \vspace*{1mm} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
434 \end{minipage} |
555
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
435 |
557
17d16700e0c8
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
436 \vspace{-3mm} |
523 | 437 |
555
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
438 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
439 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
440 %\vspace*{-3mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
441 \begin{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
442 %\hspace*{-3mm}\begin{minipage}[t]{0.18\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
443 %\centering |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
444 \vspace*{-5mm} |
558
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Fixed compilation: .PNG -> .png
Olivier Delalleau <delallea@iro>
parents:
555
diff
changeset
|
445 \includegraphics[scale=.4]{images/Distorsiongauss_only.png}\\ |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
446 {\small \bf Gauss. Noise} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
447 \end{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
448 \end{wrapfigure} |
b6dfba0a110c
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
449 %\end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
450 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
451 \vspace*{12mm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
452 The {\bf Gaussian noise} module simply adds, to each pixel of the image independently, a |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
453 noise $\sim Normal(0,(\frac{complexity}{10})^2)$. |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
454 This module is skipped with probability 70\%. |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
455 %\vspace{1.1cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
456 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
457 |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
458 \vspace*{1.2cm} |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
459 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
460 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
461 \begin{minipage}[t]{0.14\linewidth} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
462 \centering |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
463 \includegraphics[scale=.4]{images/background_other_only.png}\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
464 {\small \bf Bg Image} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
465 \end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
466 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
467 \vspace*{-18mm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
468 Following~\citet{Larochelle-jmlr-2009}, the {\bf background image} module adds a random |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
469 background image behind the letter, from a randomly chosen natural image, |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
470 with contrast adjustments depending on $complexity$, to preserve |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
471 more or less of the original character image. |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
472 %\vspace{.8cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
473 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
474 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
475 %\vspace{-.7cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
476 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
477 \begin{minipage}[t]{0.14\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
478 \centering |
558
143a1467f157
Fixed compilation: .PNG -> .png
Olivier Delalleau <delallea@iro>
parents:
555
diff
changeset
|
479 \includegraphics[scale=.4]{images/Poivresel_only.png}\\ |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
480 {\small \bf Salt \& Pepper} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
481 \end{minipage}% |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
482 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
483 \vspace*{-18mm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
484 The {\bf salt and pepper noise} module adds noise $\sim U[0,1]$ to random subsets of pixels. |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
485 The number of selected pixels is $0.2 \times complexity$. |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
486 This module is skipped with probability 75\%. |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
487 %\vspace{.9cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
488 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
489 %\vspace{-.7cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
490 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
491 \vspace{1mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
492 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
493 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
494 \begin{wrapfigure}[7]{l}{0.14\textwidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
495 %\begin{minipage}[t]{0.14\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
496 %\centering |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
497 \begin{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
498 \vspace*{-4mm} |
558
143a1467f157
Fixed compilation: .PNG -> .png
Olivier Delalleau <delallea@iro>
parents:
555
diff
changeset
|
499 \hspace*{-1mm}\includegraphics[scale=.4]{images/Rature_only.png}\\ |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
500 {\bf Scratches} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
501 %\end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
502 \end{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
503 \end{wrapfigure} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
504 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
505 %\vspace{.4cm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
506 The {\bf scratches} module places line-like white patches on the image. The |
517
0a5945249f2b
section 2, quick first pass
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
511
diff
changeset
|
507 lines are heavily transformed images of the digit ``1'' (one), chosen |
544
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
508 at random among 500 such 1 images, |
467 | 509 randomly cropped and rotated by an angle $\sim Normal(0,(100 \times |
554 | 510 complexity)^2$ (in degrees), using bi-cubic interpolation. |
495 | 511 Two passes of a grey-scale morphological erosion filter |
467 | 512 are applied, reducing the width of the line |
513 by an amount controlled by $complexity$. | |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
514 This module is skipped with probability 85\%. The probabilities |
544
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
515 of applying 1, 2, or 3 patches are (50\%,30\%,20\%). |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
516 \end{minipage} |
523 | 517 |
560
dc5c3f538a05
Small fixes (typos / precisions)
Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
518 \vspace*{1mm} |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
519 |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
520 \begin{minipage}[t]{0.25\linewidth} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
521 \centering |
559 | 522 \hspace*{-16mm}\includegraphics[scale=.4]{images/Contrast_only.png}\\ |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
523 {\bf Grey Level \& Contrast} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
524 \end{minipage}% |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
525 \hspace{-12mm}\begin{minipage}[t]{0.82\linewidth} |
560
dc5c3f538a05
Small fixes (typos / precisions)
Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
526 \vspace*{-18mm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
527 The {\bf grey level and contrast} module changes the contrast by changing grey levels, and may invert the image polarity (white |
544
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
528 to black and black to white). The contrast is $C \sim U[1-0.85 \times complexity,1]$ |
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
529 so the image is normalized into $[\frac{1-C}{2},1-\frac{1-C}{2}]$. The |
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
530 polarity is inverted with probability 50\%. |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
531 %\vspace{.7cm} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
532 \end{minipage} |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
533 \vspace{2mm} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
534 |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
535 |
499
2b58eda9fc08
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
495
diff
changeset
|
536 \iffalse |
523 | 537 \begin{figure}[ht] |
538 | 538 \centerline{\resizebox{.9\textwidth}{!}{\includegraphics{images/example_t.png}}}\\ |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
539 \caption{Illustration of the pipeline of stochastic |
519
eaa595ea2402
section 3 quickpass
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
518
diff
changeset
|
540 transformations applied to the image of a lower-case \emph{t} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
541 (the upper left image). Each image in the pipeline (going from |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
542 left to right, first top line, then bottom line) shows the result |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
543 of applying one of the modules in the pipeline. The last image |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
544 (bottom right) is used as training example.} |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
545 \label{fig:pipeline} |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
546 \end{figure} |
499
2b58eda9fc08
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
495
diff
changeset
|
547 \fi |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
548 |
560
dc5c3f538a05
Small fixes (typos / precisions)
Olivier Delalleau <delallea@iro>
parents:
559
diff
changeset
|
549 \vspace*{-3mm} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
550 \section{Experimental Setup} |
484
9a757d565e46
reduction de taille
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
483
diff
changeset
|
551 \vspace*{-1mm} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
552 |
553
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
553 Much previous work on deep learning had been performed on |
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
554 the MNIST digits task~\citep{Hinton06,ranzato-07-small,Bengio-nips-2006,Salakhutdinov+Hinton-2009}, |
472 | 555 with 60~000 examples, and variants involving 10~000 |
553
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
556 examples~\citep{Larochelle-jmlr-toappear-2008,VincentPLarochelleH2008}. |
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
557 The focus here is on much larger training sets, from 10 times to |
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
558 to 1000 times larger, and 62 classes. |
530
8fe77eac344f
Clarifying the experimental setup, typos here and there
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
524
diff
changeset
|
559 |
535 | 560 The first step in constructing the larger datasets (called NISTP and P07) is to sample from |
499
2b58eda9fc08
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
495
diff
changeset
|
561 a {\em data source}: {\bf NIST} (NIST database 19), {\bf Fonts}, {\bf Captchas}, |
2b58eda9fc08
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
495
diff
changeset
|
562 and {\bf OCR data} (scanned machine printed characters). Once a character |
547 | 563 is sampled from one of these sources (chosen randomly), the second step is to |
564 apply a pipeline of transformations and/or noise processes described in section \ref{s:perturbations}. | |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
565 |
547 | 566 To provide a baseline of error rate comparison we also estimate human performance |
567 on both the 62-class task and the 10-class digits task. | |
566 | 568 We compare the best Multi-Layer Perceptrons (MLP) against |
569 the best Stacked Denoising Auto-encoders (SDA), when | |
570 both models' hyper-parameters are selected to minimize the validation set error. | |
571 We also provide a comparison against a precise estimate | |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
572 of human performance obtained via Amazon's Mechanical Turk (AMT) |
530
8fe77eac344f
Clarifying the experimental setup, typos here and there
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
524
diff
changeset
|
573 service (http://mturk.com). |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
574 AMT users are paid small amounts |
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
575 of money to perform tasks for which human intelligence is required. |
522
d41926a68993
remis les choses qui avaient disparu
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
521
diff
changeset
|
576 Mechanical Turk has been used extensively in natural language processing and vision. |
d41926a68993
remis les choses qui avaient disparu
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
521
diff
changeset
|
577 %processing \citep{SnowEtAl2008} and vision |
d41926a68993
remis les choses qui avaient disparu
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
521
diff
changeset
|
578 %\citep{SorokinAndForsyth2008,whitehill09}. |
530
8fe77eac344f
Clarifying the experimental setup, typos here and there
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
524
diff
changeset
|
579 AMT users were presented |
553
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
580 with 10 character images (from a test set) and asked to choose 10 corresponding ASCII |
569
9d01280ff1c1
commentaires de Joseph Turian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
568
diff
changeset
|
581 characters. They were forced to choose a single character class (either among the |
9d01280ff1c1
commentaires de Joseph Turian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
568
diff
changeset
|
582 62 or 10 character classes) for each image. |
553
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
583 80 subjects classified 2500 images per (dataset,task) pair, |
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
584 with the guarantee that 3 different subjects classified each image, allowing |
550
662299f265ab
suggestions from Ian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
549
diff
changeset
|
585 us to estimate inter-human variability (e.g a standard error of 0.1\% |
553
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
586 on the average 18.2\% error done by humans on the 62-class task NIST test set). |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
587 |
553
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
588 \vspace*{-3mm} |
472 | 589 \subsection{Data Sources} |
553
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
590 \vspace*{-2mm} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
591 |
484
9a757d565e46
reduction de taille
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
483
diff
changeset
|
592 %\begin{itemize} |
9a757d565e46
reduction de taille
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
483
diff
changeset
|
593 %\item |
9a757d565e46
reduction de taille
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
483
diff
changeset
|
594 {\bf NIST.} |
501 | 595 Our main source of characters is the NIST Special Database 19~\citep{Grother-1995}, |
472 | 596 widely used for training and testing character |
516
092dae9a5040
make the reference more compact.
Frederic Bastien <nouiz@nouiz.org>
parents:
514
diff
changeset
|
597 recognition systems~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005}. |
519
eaa595ea2402
section 3 quickpass
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
518
diff
changeset
|
598 The dataset is composed of 814255 digits and characters (upper and lower cases), with hand checked classifications, |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
599 extracted from handwritten sample forms of 3600 writers. The characters are labelled by one of the 62 classes |
519
eaa595ea2402
section 3 quickpass
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
518
diff
changeset
|
600 corresponding to ``0''-``9'',``A''-``Z'' and ``a''-``z''. The dataset contains 8 parts (partitions) of varying complexity. |
534 | 601 The fourth partition (called $hsf_4$, 82587 examples), |
602 experimentally recognized to be the most difficult one, is the one recommended | |
519
eaa595ea2402
section 3 quickpass
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
518
diff
changeset
|
603 by NIST as a testing set and is used in our work as well as some previous work~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005} |
534 | 604 for that purpose. We randomly split the remainder (731668 examples) into a training set and a validation set for |
605 model selection. | |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
606 The performances reported by previous work on that dataset mostly use only the digits. |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
607 Here we use all the classes both in the training and testing phase. This is especially |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
608 useful to estimate the effect of a multi-task setting. |
553
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
609 The distribution of the classes in the NIST training and test sets differs |
8f6c09d1140f
ca fitte de nouveau
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
552
diff
changeset
|
610 substantially, with relatively many more digits in the test set, and a more uniform distribution |
566 | 611 of letters in the test set (whereas in the training set they are distributed |
553
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612 more like in natural text). |
549 | 613 \vspace*{-1mm} |
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614 |
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615 %\item |
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616 {\bf Fonts.} |
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617 In order to have a good variety of sources we downloaded an important number of free fonts from: |
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618 {\tt http://cg.scs.carleton.ca/\textasciitilde luc/freefonts.html}. |
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619 % TODO: pointless to anonymize, it's not pointing to our work |
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620 Including the operating system's (Windows 7) fonts, there is a total of $9817$ different fonts that we can choose uniformly from. |
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621 The chosen {\tt ttf} file is either used as input of the Captcha generator (see next item) or, by producing a corresponding image, |
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622 directly as input to our models. |
549 | 623 \vspace*{-1mm} |
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624 |
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625 %\item |
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626 {\bf Captchas.} |
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627 The Captcha data source is an adaptation of the \emph{pycaptcha} library (a python based captcha generator library) for |
472 | 628 generating characters of the same format as the NIST dataset. This software is based on |
495 | 629 a random character class generator and various kinds of transformations similar to those described in the previous sections. |
472 | 630 In order to increase the variability of the data generated, many different fonts are used for generating the characters. |
495 | 631 Transformations (slant, distortions, rotation, translation) are applied to each randomly generated character with a complexity |
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632 depending on the value of the complexity parameter provided by the user of the data source. |
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633 %Two levels of complexity are allowed and can be controlled via an easy to use facade class. %TODO: what's a facade class? |
549 | 634 \vspace*{-1mm} |
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635 |
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636 %\item |
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637 {\bf OCR data.} |
472 | 638 A large set (2 million) of scanned, OCRed and manually verified machine-printed |
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639 characters where included as an |
472 | 640 additional source. This set is part of a larger corpus being collected by the Image Understanding |
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641 Pattern Recognition Research group led by Thomas Breuel at University of Kaiserslautern |
495 | 642 ({\tt http://www.iupr.com}), and which will be publicly released. |
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643 %TODO: let's hope that Thomas is not a reviewer! :) Seriously though, maybe we should anonymize this |
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644 %\end{itemize} |
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645 |
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646 \vspace*{-3mm} |
472 | 647 \subsection{Data Sets} |
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648 \vspace*{-2mm} |
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649 |
472 | 650 All data sets contain 32$\times$32 grey-level images (values in $[0,1]$) associated with a label |
651 from one of the 62 character classes. | |
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652 %\begin{itemize} |
549 | 653 \vspace*{-1mm} |
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654 |
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655 %\item |
534 | 656 {\bf NIST.} This is the raw NIST special database 19~\citep{Grother-1995}. It has |
535 | 657 \{651668 / 80000 / 82587\} \{training / validation / test\} examples. |
549 | 658 \vspace*{-1mm} |
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659 |
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660 %\item |
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661 {\bf P07.} This dataset is obtained by taking raw characters from all four of the above sources |
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662 and sending them through the transformation pipeline described in section \ref{s:perturbations}. |
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663 For each new example to generate, a data source is selected with probability $10\%$ from the fonts, |
472 | 664 $25\%$ from the captchas, $25\%$ from the OCR data and $40\%$ from NIST. We apply all the transformations in the |
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665 order given above, and for each of them we sample uniformly a \emph{complexity} in the range $[0,0.7]$. |
535 | 666 It has \{81920000 / 80000 / 20000\} \{training / validation / test\} examples. |
549 | 667 \vspace*{-1mm} |
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668 |
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669 %\item |
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670 {\bf NISTP.} This one is equivalent to P07 (complexity parameter of $0.7$ with the same proportions of data sources) |
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671 except that we only apply |
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672 transformations from slant to pinch. Therefore, the character is |
495 | 673 transformed but no additional noise is added to the image, giving images |
534 | 674 closer to the NIST dataset. |
535 | 675 It has \{81920000 / 80000 / 20000\} \{training / validation / test\} examples. |
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676 %\end{itemize} |
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677 |
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678 \vspace*{-3mm} |
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679 \subsection{Models and their Hyperparameters} |
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680 \vspace*{-2mm} |
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681 |
566 | 682 The experiments are performed using MLPs (with a single |
683 hidden layer) and SDAs. | |
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684 \emph{Hyper-parameters are selected based on the {\bf NISTP} validation set error.} |
472 | 685 |
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686 {\bf Multi-Layer Perceptrons (MLP).} |
472 | 687 Whereas previous work had compared deep architectures to both shallow MLPs and |
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688 SVMs, we only compared to MLPs here because of the very large datasets used |
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689 (making the use of SVMs computationally challenging because of their quadratic |
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690 scaling behavior). |
472 | 691 The MLP has a single hidden layer with $\tanh$ activation functions, and softmax (normalized |
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692 exponentials) on the output layer for estimating $P(class | image)$. |
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693 The number of hidden units is taken in $\{300,500,800,1000,1500\}$. |
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694 Training examples are presented in minibatches of size 20. A constant learning |
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695 rate was chosen among $\{0.001, 0.01, 0.025, 0.075, 0.1, 0.5\}$. |
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696 %through preliminary experiments (measuring performance on a validation set), |
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697 %and $0.1$ (which was found to work best) was then selected for optimizing on |
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698 %the whole training sets. |
549 | 699 \vspace*{-1mm} |
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700 |
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701 |
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702 {\bf Stacked Denoising Auto-Encoders (SDA).} |
472 | 703 Various auto-encoder variants and Restricted Boltzmann Machines (RBMs) |
704 can be used to initialize the weights of each layer of a deep MLP (with many hidden | |
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705 layers)~\citep{Hinton06,ranzato-07-small,Bengio-nips-2006}, |
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706 apparently setting parameters in the |
472 | 707 basin of attraction of supervised gradient descent yielding better |
708 generalization~\citep{Erhan+al-2010}. It is hypothesized that the | |
709 advantage brought by this procedure stems from a better prior, | |
710 on the one hand taking advantage of the link between the input | |
711 distribution $P(x)$ and the conditional distribution of interest | |
712 $P(y|x)$ (like in semi-supervised learning), and on the other hand | |
713 taking advantage of the expressive power and bias implicit in the | |
714 deep architecture (whereby complex concepts are expressed as | |
715 compositions of simpler ones through a deep hierarchy). | |
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716 |
547 | 717 \begin{figure}[ht] |
718 \vspace*{-2mm} | |
719 \centerline{\resizebox{0.8\textwidth}{!}{\includegraphics{images/denoising_autoencoder_small.pdf}}} | |
549 | 720 \vspace*{-2mm} |
547 | 721 \caption{Illustration of the computations and training criterion for the denoising |
722 auto-encoder used to pre-train each layer of the deep architecture. Input $x$ of | |
723 the layer (i.e. raw input or output of previous layer) | |
569
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724 s corrupted into $\tilde{x}$ and encoded into code $y$ by the encoder $f_\theta(\cdot)$. |
547 | 725 The decoder $g_{\theta'}(\cdot)$ maps $y$ to reconstruction $z$, which |
726 is compared to the uncorrupted input $x$ through the loss function | |
727 $L_H(x,z)$, whose expected value is approximately minimized during training | |
728 by tuning $\theta$ and $\theta'$.} | |
729 \label{fig:da} | |
730 \vspace*{-2mm} | |
731 \end{figure} | |
732 | |
472 | 733 Here we chose to use the Denoising |
566 | 734 Auto-encoder~\citep{VincentPLarochelleH2008} as the building block for |
569
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735 these deep hierarchies of features, as it is simple to train and |
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736 explain (see Figure~\ref{fig:da}, as well as |
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737 tutorial and code there: {\tt http://deeplearning.net/tutorial}), |
566 | 738 provides efficient inference, and yielded results |
472 | 739 comparable or better than RBMs in series of experiments |
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740 \citep{VincentPLarochelleH2008}. During training, a Denoising |
566 | 741 Auto-encoder is presented with a stochastically corrupted version |
472 | 742 of the input and trained to reconstruct the uncorrupted input, |
743 forcing the hidden units to represent the leading regularities in | |
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744 the data. Here we use the random binary masking corruption |
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745 (which sets to 0 a random subset of the inputs). |
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746 Once it is trained, in a purely unsupervised way, |
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747 its hidden units' activations can |
472 | 748 be used as inputs for training a second one, etc. |
749 After this unsupervised pre-training stage, the parameters | |
750 are used to initialize a deep MLP, which is fine-tuned by | |
751 the same standard procedure used to train them (see previous section). | |
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752 The SDA hyper-parameters are the same as for the MLP, with the addition of the |
472 | 753 amount of corruption noise (we used the masking noise process, whereby a |
754 fixed proportion of the input values, randomly selected, are zeroed), and a | |
755 separate learning rate for the unsupervised pre-training stage (selected | |
756 from the same above set). The fraction of inputs corrupted was selected | |
757 among $\{10\%, 20\%, 50\%\}$. Another hyper-parameter is the number | |
758 of hidden layers but it was fixed to 3 based on previous work with | |
547 | 759 SDAs on MNIST~\citep{VincentPLarochelleH2008}. |
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760 |
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761 \vspace*{-1mm} |
523 | 762 |
763 \begin{figure}[ht] | |
541 | 764 \vspace*{-2mm} |
523 | 765 \centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/error_rates_charts.pdf}}} |
549 | 766 \vspace*{-3mm} |
547 | 767 \caption{SDAx are the {\bf deep} models. Error bars indicate a 95\% confidence interval. 0 indicates that the model was trained |
523 | 768 on NIST, 1 on NISTP, and 2 on P07. Left: overall results |
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769 of all models, on NIST and NISTP test sets. |
523 | 770 Right: error rates on NIST test digits only, along with the previous results from |
771 literature~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005} | |
772 respectively based on ART, nearest neighbors, MLPs, and SVMs.} | |
773 \label{fig:error-rates-charts} | |
541 | 774 \vspace*{-2mm} |
523 | 775 \end{figure} |
776 | |
777 | |
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778 \begin{figure}[ht] |
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779 \vspace*{-3mm} |
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780 \centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/improvements_charts.pdf}}} |
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781 \vspace*{-3mm} |
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782 \caption{Relative improvement in error rate due to self-taught learning. |
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783 Left: Improvement (or loss, when negative) |
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784 induced by out-of-distribution examples (perturbed data). |
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785 Right: Improvement (or loss, when negative) induced by multi-task |
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786 learning (training on all classes and testing only on either digits, |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
787 upper case, or lower-case). The deep learner (SDA) benefits more from |
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
788 both self-taught learning scenarios, compared to the shallow MLP.} |
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
789 \label{fig:improvements-charts} |
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
790 \vspace*{-2mm} |
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
791 \end{figure} |
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
792 |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
793 \section{Experimental Results} |
549 | 794 \vspace*{-2mm} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
795 |
485
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
796 %\vspace*{-1mm} |
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
797 %\subsection{SDA vs MLP vs Humans} |
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
798 %\vspace*{-1mm} |
535 | 799 The models are either trained on NIST (MLP0 and SDA0), |
800 NISTP (MLP1 and SDA1), or P07 (MLP2 and SDA2), and tested | |
550
662299f265ab
suggestions from Ian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
549
diff
changeset
|
801 on either NIST, NISTP or P07, either on the 62-class task |
568 | 802 or on the 10-digits task. Training (including about half |
803 for unsupervised pre-training, for DAs) on the larger | |
804 datasets takes around one day on a GPU-285. | |
485
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
805 Figure~\ref{fig:error-rates-charts} summarizes the results obtained, |
550
662299f265ab
suggestions from Ian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
549
diff
changeset
|
806 comparing humans, the three MLPs (MLP0, MLP1, MLP2) and the three SDAs (SDA0, SDA1, |
486
877af97ee193
section resultats et appendice
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
485
diff
changeset
|
807 SDA2), along with the previous results on the digits NIST special database |
566 | 808 19 test set from the literature, respectively based on ARTMAP neural |
486
877af97ee193
section resultats et appendice
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
485
diff
changeset
|
809 networks ~\citep{Granger+al-2007}, fast nearest-neighbor search |
516
092dae9a5040
make the reference more compact.
Frederic Bastien <nouiz@nouiz.org>
parents:
514
diff
changeset
|
810 ~\citep{Cortes+al-2000}, MLPs ~\citep{Oliveira+al-2002-short}, and SVMs |
486
877af97ee193
section resultats et appendice
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
485
diff
changeset
|
811 ~\citep{Milgram+al-2005}. More detailed and complete numerical results |
493
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
812 (figures and tables, including standard errors on the error rates) can be |
535 | 813 found in Appendix I of the supplementary material. |
814 The deep learner not only outperformed the shallow ones and | |
493
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
815 previously published performance (in a statistically and qualitatively |
535 | 816 significant way) but when trained with perturbed data |
817 reaches human performance on both the 62-class task | |
523 | 818 and the 10-class (digits) task. |
550
662299f265ab
suggestions from Ian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
549
diff
changeset
|
819 17\% error (SDA1) or 18\% error (humans) may seem large but a large |
662299f265ab
suggestions from Ian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
549
diff
changeset
|
820 majority of the errors from humans and from SDA1 are from out-of-context |
662299f265ab
suggestions from Ian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
549
diff
changeset
|
821 confusions (e.g. a vertical bar can be a ``1'', an ``l'' or an ``L'', and a |
662299f265ab
suggestions from Ian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
549
diff
changeset
|
822 ``c'' and a ``C'' are often indistinguishible). |
523 | 823 |
824 In addition, as shown in the left of | |
825 Figure~\ref{fig:improvements-charts}, the relative improvement in error | |
493
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
826 rate brought by self-taught learning is greater for the SDA, and these |
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
827 differences with the MLP are statistically and qualitatively |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
828 significant. |
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
829 The left side of the figure shows the improvement to the clean |
493
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
830 NIST test set error brought by the use of out-of-distribution examples |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
831 (i.e. the perturbed examples examples from NISTP or P07). |
547 | 832 Relative percent change is measured by taking |
548
34cb28249de0
suggestions de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
547
diff
changeset
|
833 $100 \% \times$ (original model's error / perturbed-data model's error - 1). |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
834 The right side of |
523 | 835 Figure~\ref{fig:improvements-charts} shows the relative improvement |
486
877af97ee193
section resultats et appendice
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
485
diff
changeset
|
836 brought by the use of a multi-task setting, in which the same model is |
877af97ee193
section resultats et appendice
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
485
diff
changeset
|
837 trained for more classes than the target classes of interest (i.e. training |
877af97ee193
section resultats et appendice
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
485
diff
changeset
|
838 with all 62 classes when the target classes are respectively the digits, |
493
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
839 lower-case, or upper-case characters). Again, whereas the gain from the |
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
840 multi-task setting is marginal or negative for the MLP, it is substantial |
547 | 841 for the SDA. Note that to simplify these multi-task experiments, only the original |
493
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
842 NIST dataset is used. For example, the MLP-digits bar shows the relative |
547 | 843 percent improvement in MLP error rate on the NIST digits test set |
566 | 844 is $100\% \times$ (single-task |
845 model's error / multi-task model's error - 1). The single-task model is | |
493
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
846 trained with only 10 outputs (one per digit), seeing only digit examples, |
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
847 whereas the multi-task model is trained with 62 outputs, with all 62 |
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
848 character classes as examples. Hence the hidden units are shared across |
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
849 all tasks. For the multi-task model, the digit error rate is measured by |
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
850 comparing the correct digit class with the output class associated with the |
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
851 maximum conditional probability among only the digit classes outputs. The |
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
852 setting is similar for the other two target classes (lower case characters |
a194ce5a4249
difference stat. sign.
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
491
diff
changeset
|
853 and upper case characters). |
485
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
854 %\vspace*{-1mm} |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
855 %\subsection{Perturbed Training Data More Helpful for SDA} |
485
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
856 %\vspace*{-1mm} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
857 |
485
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
858 %\vspace*{-1mm} |
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
859 %\subsection{Multi-Task Learning Effects} |
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
860 %\vspace*{-1mm} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
861 |
485
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
862 \iffalse |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
863 As previously seen, the SDA is better able to benefit from the |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
864 transformations applied to the data than the MLP. In this experiment we |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
865 define three tasks: recognizing digits (knowing that the input is a digit), |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
866 recognizing upper case characters (knowing that the input is one), and |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
867 recognizing lower case characters (knowing that the input is one). We |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
868 consider the digit classification task as the target task and we want to |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
869 evaluate whether training with the other tasks can help or hurt, and |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
870 whether the effect is different for MLPs versus SDAs. The goal is to find |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
871 out if deep learning can benefit more (or less) from multiple related tasks |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
872 (i.e. the multi-task setting) compared to a corresponding purely supervised |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
873 shallow learner. |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
874 |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
875 We use a single hidden layer MLP with 1000 hidden units, and a SDA |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
876 with 3 hidden layers (1000 hidden units per layer), pre-trained and |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
877 fine-tuned on NIST. |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
878 |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
879 Our results show that the MLP benefits marginally from the multi-task setting |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
880 in the case of digits (5\% relative improvement) but is actually hurt in the case |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
881 of characters (respectively 3\% and 4\% worse for lower and upper class characters). |
495 | 882 On the other hand the SDA benefited from the multi-task setting, with relative |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
883 error rate improvements of 27\%, 15\% and 13\% respectively for digits, |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
884 lower and upper case characters, as shown in Table~\ref{tab:multi-task}. |
485
6beaf3328521
les tables enlevées
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
484
diff
changeset
|
885 \fi |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
886 |
475 | 887 |
549 | 888 \vspace*{-2mm} |
529 | 889 \section{Conclusions and Discussion} |
549 | 890 \vspace*{-2mm} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
891 |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
892 We have found that the self-taught learning framework is more beneficial |
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
893 to a deep learner than to a traditional shallow and purely |
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
894 supervised learner. More precisely, |
520
18a6379999fd
more after lunch :)
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
519
diff
changeset
|
895 the answers are positive for all the questions asked in the introduction. |
484
9a757d565e46
reduction de taille
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
483
diff
changeset
|
896 %\begin{itemize} |
487 | 897 |
484
9a757d565e46
reduction de taille
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
483
diff
changeset
|
898 $\bullet$ %\item |
547 | 899 {\bf Do the good results previously obtained with deep architectures on the |
549 | 900 MNIST digits generalize to a much larger and richer (but similar) |
547 | 901 dataset, the NIST special database 19, with 62 classes and around 800k examples}? |
569
9d01280ff1c1
commentaires de Joseph Turian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
568
diff
changeset
|
902 Yes, the SDA {\em systematically outperformed the MLP and all the previously |
9d01280ff1c1
commentaires de Joseph Turian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
568
diff
changeset
|
903 published results on this dataset} (the ones that we are aware of), {\em in fact reaching human-level |
529 | 904 performance} at around 17\% error on the 62-class task and 1.4\% on the digits. |
484
9a757d565e46
reduction de taille
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
483
diff
changeset
|
905 |
9a757d565e46
reduction de taille
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
483
diff
changeset
|
906 $\bullet$ %\item |
547 | 907 {\bf To what extent do self-taught learning scenarios help deep learners, |
908 and do they help them more than shallow supervised ones}? | |
529 | 909 We found that distorted training examples not only made the resulting |
910 classifier better on similarly perturbed images but also on | |
911 the {\em original clean examples}, and more importantly and more novel, | |
912 that deep architectures benefit more from such {\em out-of-distribution} | |
913 examples. MLPs were helped by perturbed training examples when tested on perturbed input | |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
914 images (65\% relative improvement on NISTP) |
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
915 but only marginally helped (5\% relative improvement on all classes) |
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
916 or even hurt (10\% relative loss on digits) |
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
917 with respect to clean examples . On the other hand, the deep SDAs |
569
9d01280ff1c1
commentaires de Joseph Turian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
568
diff
changeset
|
918 were significantly boosted by these out-of-distribution examples. |
529 | 919 Similarly, whereas the improvement due to the multi-task setting was marginal or |
502
2b35a6e5ece4
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
501
diff
changeset
|
920 negative for the MLP (from +5.6\% to -3.6\% relative change), |
569
9d01280ff1c1
commentaires de Joseph Turian
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
568
diff
changeset
|
921 it was quite significant for the SDA (from +13\% to +27\% relative change), |
547 | 922 which may be explained by the arguments below. |
484
9a757d565e46
reduction de taille
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
483
diff
changeset
|
923 %\end{itemize} |
472 | 924 |
524
07bc0ca8d246
added paragraph comparing "our" self-taught learning with "theirs"
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
523
diff
changeset
|
925 In the original self-taught learning framework~\citep{RainaR2007}, the |
07bc0ca8d246
added paragraph comparing "our" self-taught learning with "theirs"
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
523
diff
changeset
|
926 out-of-sample examples were used as a source of unsupervised data, and |
07bc0ca8d246
added paragraph comparing "our" self-taught learning with "theirs"
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
523
diff
changeset
|
927 experiments showed its positive effects in a \emph{limited labeled data} |
07bc0ca8d246
added paragraph comparing "our" self-taught learning with "theirs"
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
523
diff
changeset
|
928 scenario. However, many of the results by \citet{RainaR2007} (who used a |
550
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929 shallow, sparse coding approach) suggest that the {\em relative gain of self-taught |
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930 learning vs ordinary supervised learning} diminishes as the number of labeled examples increases. |
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931 We note instead that, for deep |
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932 architectures, our experiments show that such a positive effect is accomplished |
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933 even in a scenario with a \emph{large number of labeled examples}, |
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934 i.e., here, the relative gain of self-taught learning is probably preserved |
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935 in the asymptotic regime. |
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936 |
547 | 937 {\bf Why would deep learners benefit more from the self-taught learning framework}? |
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938 The key idea is that the lower layers of the predictor compute a hierarchy |
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939 of features that can be shared across tasks or across variants of the |
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940 input distribution. Intermediate features that can be used in different |
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941 contexts can be estimated in a way that allows to share statistical |
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942 strength. Features extracted through many levels are more likely to |
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943 be more abstract (as the experiments in~\citet{Goodfellow2009} suggest), |
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944 increasing the likelihood that they would be useful for a larger array |
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945 of tasks and input conditions. |
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946 Therefore, we hypothesize that both depth and unsupervised |
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947 pre-training play a part in explaining the advantages observed here, and future |
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948 experiments could attempt at teasing apart these factors. |
529 | 949 And why would deep learners benefit from the self-taught learning |
950 scenarios even when the number of labeled examples is very large? | |
951 We hypothesize that this is related to the hypotheses studied | |
952 in~\citet{Erhan+al-2010}. Whereas in~\citet{Erhan+al-2010} | |
953 it was found that online learning on a huge dataset did not make the | |
954 advantage of the deep learning bias vanish, a similar phenomenon | |
955 may be happening here. We hypothesize that unsupervised pre-training | |
956 of a deep hierarchy with self-taught learning initializes the | |
957 model in the basin of attraction of supervised gradient descent | |
958 that corresponds to better generalization. Furthermore, such good | |
959 basins of attraction are not discovered by pure supervised learning | |
960 (with or without self-taught settings), and more labeled examples | |
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961 does not allow the model to go from the poorer basins of attraction discovered |
529 | 962 by the purely supervised shallow models to the kind of better basins associated |
963 with deep learning and self-taught learning. | |
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964 |
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965 A Flash demo of the recognizer (where both the MLP and the SDA can be compared) |
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966 can be executed on-line at {\tt http://deep.host22.com}. |
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967 |
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968 \newpage |
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969 { |
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970 \bibliography{strings,strings-short,strings-shorter,ift6266_ml,aigaion-shorter,specials} |
469 | 971 %\bibliographystyle{plainnat} |
972 \bibliographystyle{unsrtnat} | |
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973 %\bibliographystyle{apalike} |
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974 } |
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975 |
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976 |
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977 \end{document} |